Natural language processing involves processing of a natural language input. A natural language input may be understood as a human language used by a person to interact with a computing system. An example of the natural language input may be “A Raven gave me a punch”. As can be understood, each word or phrase in this sentence may have different alternative interpretations, which make meaning of the input ambiguous. For example, “A Raven” can be a bird, a football player, or a person with raven-colored hair. Further, “me” can be an object form of the pronoun ‘I’ or “me” may also refer to “Medical Emergency”, “Medical Examiner”, or “Mechanical Engineer”. Likewise, “punch” could mean a hit in the mouth, a type of drink, or a device to make holes in an object. Also, “give” could be an act of property transfer, and act of labeling, or a physical action, depending on the precise meaning of “raven” and “punch”.
The computing system may attempt to take an action based on the natural language input. However, in order to do so, the computing system must determine the precise meaning of the natural language input such that the natural language input can be understood and acted upon by the computing system. In order to be successful, the computing system must understand the question precisely enough to identify specific data records (for example, representing a person, action or product) to operate upon. Further, when such precision is not possible (due, for example, to ambiguous statements by the human) the computing system must be able to understand that the input is ambiguous, understand the alternative interpretations and allow for methods for resolving the ambiguity using methods such as statistical or probabilistic analysis, machine learning, confidence analysis or simply asking a human user to resolve the ambiguity.
Various natural language processing systems have been developed in the past. However, such natural language processing systems lack intelligence and a technically advanced framework for representing, managing, and selecting from ambiguous interpretations in a natural language input. This is because, in existing systems, alternative interpretations of words are pruned at every step. For example, for the natural language input “The Raven gave me a punch”, the conventional natural language processing systems make a fixed choice for each word at an initial stage, and thus discard a range of possible interpretations.
For example, available techniques may lower-case all words. For instance, “Raven” in the example above, may be converted to “raven”, thereby discarding the Title-case version of the word. As a result, the likelihood that “Raven” can refer to a member of the Raven's football team, i.e., a proper noun, is reduced or eliminated. Such simplifications may occur at various levels in available tools. For instance, simplifications with respect to word classification (e.g. punch as a verb vs. punch as a noun), syntax (attachment of phrases) and semantics (semantic interpretations). Such simplifications and pruning are cumulative, which in turn may reduce the accuracy and coverage of available natural language processing tools and may prevent such tools from being able to represent the entire range of human language richness.
Owing to the elimination of possible interpretations at an early state, the ability of available natural language processing systems to determine the appropriate interpretation of the natural language input may be significantly reduced. Therefore, because all the alternative interpretations are not taken into consideration while processing the natural language input, existing natural language processing systems may not correctly understand the natural language input. Accordingly, a technical problem with the currently available natural language processing systems is that they may be inefficient, inaccurate, incomplete, and unreliable.